Related papers: User-specific, Adaptable Safety Controllers Facili…
In physical human-robot interaction, the coexistence of robots and humans in the same workspace requires the guarantee of a stable interaction, trying to minimize the effort for the operator. To this aim, the admittance control is widely…
An outstanding challenge for the widespread deployment of robotic systems like autonomous vehicles is ensuring safe interaction with humans without sacrificing performance. Existing safety methods often neglect the robot's ability to learn…
Learning from human feedback is a popular approach to train robots to adapt to user preferences and improve safety. Existing approaches typically consider a single querying (interaction) format when seeking human feedback and do not…
Trust is a multilayered concept with critical relevance when it comes to introducing new technologies. Understanding how humans will interact with complex vehicle systems and preparing for the functional, societal and psychological aspects…
The interest in using reinforcement learning (RL) controllers in safety-critical applications such as robot navigation around pedestrians motivates the development of additional safety mechanisms. Running RL-enabled systems among uncertain…
Assistive robotic arms often have more degrees-of-freedom than a human teleoperator can control with a low-dimensional input, like a joystick. To overcome this challenge, existing approaches use data-driven methods to learn a mapping from…
There is invariably a trade-off between safety and efficiency for collaborative robots (cobots) in human-robot collaborations. Robots that interact minimally with humans can work with high speed and accuracy but cannot adapt to new tasks or…
The recent adoption of artificial intelligence in robotics has driven the development of algorithms that enable autonomous systems to adapt to complex social environments. In particular, safe and efficient social navigation is a key…
Robotaxis are emerging as a promising form of urban mobility, but removing human drivers fundamentally reshapes passenger-vehicle interaction and raises new design challenges. To inform robotaxi design based on real-world experience, we…
As robots are increasingly deployed in real-world scenarios, a key question is how to best transfer knowledge learned in one environment to another, where shifting constraints and human preferences render adaptation challenging. A central…
While artificial-intelligence-based methods suffer from lack of transparency, rule-based methods dominate in safety-critical systems. Yet, the latter cannot compete with the first ones in robustness to multiple requirements, for instance,…
Social robots hold promise for reducing job interview anxiety, yet designing agents that provide both psychological safety and instructional guidance remains challenging. Through a three-phase iterative design study (N = 8), we empirically…
As of today, robots exhibit impressive agility but also pose potential hazards to humans using/collaborating with them. Consequently, safety is considered the most paramount factor in human-robot interaction (HRI). This paper presents a…
The role of robots is expanding from tool to collaborator. Socially assistive robots (SARs) are an example of collaborative robots that assist humans in the real world. As robots enter our social sphere, unforeseen risks occur during…
With recent advancements in AI and computation tools, intelligent paradigms emerged to empower different fields such as healthcare robots with new capabilities. Advanced AI robotic algorithms (e.g., reinforcement learning) can be trained…
In Human-Robot Collaboration, the robot operates in a highly dynamic environment. Thus, it is pivotal to guarantee the robust stability of the system during the interaction but also a high flexibility of the robot behavior in order to…
Achieving long-term safety in uncertain/extreme environments while accounting for human preferences remains a fundamental challenge for autonomous systems. Existing methods often trade off long-term guarantees for fast real-time control and…
To interact seamlessly with robots, users must infer the causes of a robot's behavior and be confident about that inference. Hence, trust is a necessary condition for human-robot collaboration (HRC). Despite its crucial role, it is largely…
A critical goal of autonomy and artificial intelligence is enabling autonomous robots to rapidly adapt in dynamic and uncertain environments. Classic adaptive control and safe control provide stability and safety guarantees but are limited…
Many tasks, particularly those involving interaction with the environment, are characterized by high variability, making robotic autonomy difficult. One flexible solution is to introduce the input of a human with superior experience and…